公开(公告)号：US20220366263A1

公开(公告)日：2022-11-17

申请号：US17313655

申请日：2021-05-06

Applicant: Waymo LLC

Inventor： Ming Ji ,  Edward Stephen Walker, JR. ,  Yang Song ,  Zijian Guo ,  Congcong Li

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a student machine learning model using a teacher machine learning model that has a pre-trained feature extractor. In one aspect, a method includes obtaining data specifying the teacher machine learning model that is configured to perform a machine learning task; obtaining first training data; training the teacher machine learning model on the first training data to obtain a trained teacher machine learning model; generating second, automatically labeled training data by using the trained teacher machine learning model to process unlabeled training data; and training a student machine learning model to perform the machine learning task using at least the second, automatically labeled training data, wherein the student machine learning model does not include the pre-trained feature extractor and instead includes a different feature extractor having fewer parameters than the pre-trained feature extractor.

公开(公告)号：US20230099920A1

公开(公告)日：2023-03-30

申请号：US17994991

申请日：2022-11-28

Applicant: Waymo LLC

Inventor： Junhua Mao ,  Lo Po Tsui ,  Congcong Li ,  Edward Stephen Walker, JR.

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a classifier to detect open vehicle doors. One of the methods includes obtaining a plurality of initial training examples, each initial training example comprising (i) a sensor sample from a collection of sensor samples and (ii) data classifying the sensor sample as characterizing a vehicle that has an open door; generating a plurality of additional training examples, comprising, for each initial training example: identifying, from the collection of sensor samples, one or more additional sensor samples that were captured less than a threshold amount of time before the sensor sample in the initial training example was captured; and training the machine learning classifier on first training data that includes the initial training examples and the additional training examples to generate updated weights for the machine learning classifier.